Infrared Thermography for Land Mine Detection

Abstract

This chapter introduces the application of infrared (IR) thermography in land mine detection. IR thermography in general and for remotely detecting buried land mines in particular, seems to be a promising diagnostic tool. Due to the difference in thermophysical properties of mines and the soil (mines retain or release heat at a rate different from the soil), soil-surface thermal contrasts above the mines are formed. These contrasts are captured by IR cameras to show the changes in temperature over the mines, which can be used for detecting them. Clearly, the degree of success of such detection technology depends on the factors that affect the formation of the thermal contrasts (signatures), such as the depth of burial; soil properties and attributes, including mine properties (size); as well as the time of day during which the measurement is carried out. Another important factor that strongly influences the viability of IR detection method is the rate of false alarms. Indeed, IR sensors may detect any thermally transmitting objects, not only land mines. It is therefore necessary to develop parameter estimation and decision-making tools that enable the IR technology to distinguish signals resulting from a land mine and unrelated clutter signals. This chapter consists of four sections. The first section introduces the physical principles of IR thermography and gives an overview of its literature. The flowchart of the technique is also given in this section. In the second one, we summarize a thermal model of the soil with the presence of shallowly buried objects. This model is used for studying the influence of soil and land mine properties on the temperature distribution of the soil, especially on its surface. The third section aims at detecting possible anomalies in the soil using IR images and classifying them as mines or nonmine objects. The classification is based on the estimation of the thermal and geometric properties of the detected anomalies. In the fourth section, the performance, in terms of probability of detection and false alarm rates, of the proposed approach for an experimental data set is presented. The processing chain of IR thermography, including data acquisition, data preprocessing, anomaly detection, and estimation of thermal and geometric properties of the detected anomalies, is presented and illustrated using an experimental data set measured in an outdoor minefield. Finally, conclusions on the statistical reliability of the IR technique are drawn.